Language Modeling for Soft Keyboards

Joshua Goodman and Gina Venolia, Microsoft Research; Keith Steury, Microsoft Corporation; Chauncey Parker, University of Washington

We describe how language models, combined with models of pen placement, can be used to significantly reduce the error rate of soft keyboard usage, by allowing for cases in which a key press is outside of a key boundary. Language models predict the probabilities of words or letters. Soft keyboards are images of keyboards on a touch screen used for input on Personal Digital Assistants. When a soft keyboard user hits a key near the boundary of a key position, we can use the language model and key press model to select the most probable key sequence, rather than the sequence dictated by strict key boundaries. This leads to an overall error rate reduction by a factor of 1.67 to 1.87.


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